Causality Reading Group (archive)

This page archives the meetings of the Causality Reading Group from 2014 to early 2016. The new schedule for the reading group has moved to the AMLab website.

Schedule 2016

DateArticleDiscussant
2016/01/08Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing by Benjamini and HochbergPhilip Versteeg

Schedule 2015

DateArticleDiscussant
2015/01/30Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors by M. Drton, M. Eichler, T. S. RichardsonNicholas Cornia
2015/02/13Constraint-based Causal Discovery: Conflict Resolution with Answer Set Programming, and supplement, by A. Hyttinen, F. Eberhardt, and M. JärvisaloSara Magliacane
2015/02/20Enriching for direct regulatory targets in perturbed gene-expression profiles by S. G. Tringe, A. Wagner, S. W. RubyPhilip Versteeg
2015/02/27Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs by A. Hauser and P. BühlmannJoris Mooij
2015/03/06Causal Discovery from Changes by J. Tian and J. PearlSara Magliacane
2015/03/13Causal Discovery from Changes: a Bayesian Approach by J. Tian and J. PearlPhilip Versteeg
2015/03/20Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets (pp. 1-12) by S. Triantafillou and I. TsamardinosNicholas Cornia
2015/03/27Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets (pp. 13-22) by S. Triantafillou and I. TsamardinosPhilip Versteeg
2015/04/16Constraint-based Causal Discovery from Multiple Interventions over Overlapping Variable Sets (pp. 23-47) by S. Triantafillou and I. Tsamardinos; Statistical significance for genomewide studies by J.D. Storey and R. TibshiraniJoris Mooij
2015/06/12Feedback models interpretation and discovery, chapter 2 of PhD thesis of Thomas RichardsonMartin Gullaksen
2015/07/03Advances in Bayesian Network Learning using Integer Programming by Bartlett and CussensSara Magliacane
2015/07/24backShift: Learning causal cyclic graphs from unknown shift interventions by Rothenhäsler, Heinze, Peters, MeinshausenJoris Mooij
2015/07/31Computing Maximum Likelihood Estimates in Recursive Linear Models with Correlated Errors by M. Drton, M. Eichler, and T. S. RichardsonJoris Mooij
2015/08/28Single timepoint models of dynamic systems by K. Sachs, S. Itani, J. Fitzgerald, B. Schoeberl, G.P. Nolan, C.J. TomlinJoris Mooij
2015/09/11UAI tutorial Non-parametric Causal Models by Richardson and Evans (part 1a; slides, video)-
2015/09/18UAI tutorial Non-parametric Causal Models by Richardson and Evans (part 1b; slides, video)-
2015/09/25--
2015/09/30Studies in Causal Reasoning and Learning (ch. 1, 4.1, 4.2, 4.3) by Jin TianJoris Mooij
2015/10/02--
2015/10/09Influence Diagrams by Howard and Matheson & Influence Diagrams - Historical and Personal Perspectives by Pearl & Influence Diagrams for Causal Modelling and Inference by A.P. DawidDiederik Roijers
2015/10/16Distribution-Free Learning of Bayesian Network Structure in Continuous Domains by D. MargaritisSara Magliacane
2015/10/23Graphs for margins of Bayesian networks (without section 6) by R. EvansStephan Bongers
2015/11/06unspecifiedDiederik Roijers
2015/11/13Inferring latent structures via information inequalities by Chaves, Luft, Maciel, Gross, Janzing and SchölkopfPhilip Versteeg
2015/11/30 11:00-12:00Independence Properties of Directed Markov Fields by Lauritzen, Dawid, Larsen, LeimerTBA
2015/12/4--
2015/12/11Single World Intervention Graphs: A Primer by Richardson and RobinsJoris
2015/12/22Single World Intervention Graphs: A Primer by Richardson and RobinsTBA

Schedule 2014

DateArticleDiscussant
2014/05/23Chain graph models and their causal interpretations by S.L. Lauritzen and T.S. RichardsonJoris Mooij
2014/06/13Classification using Discriminative Restricted Boltzmann Machines by H. Larochelle and Y. BengioSergio Mota

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